My tracks in the class corpus
This is a graph which has mapped the engagingness of each song compared to its danceability. The colour scale is based on the tempo of each song. The first noticable aspect of the graph is the seemingly positive correlation between danceability and engagingness which is shown by the red trend line. On average it is clear that in most cases a high danceability value means that same song will have a high engagingness rating aswell. From the colour scaling it can also be noticed that most songs that have high scores for those features also have a higher tempo. This could mean that those features are highly correlated or that the way essentia measured these features is similar in terms of computational analysis. It would be interesting to look at why this correlation seems to be in place, for instance through examining the roll of instrumentallness, or genre in combination with this analysis.
The two points that are highlighted are a song I arranged by myself and one I genreated with suno. What can be seen with these songs is that my own song performs higher in both danceability and engagingness than the ai song while they are the same genre and made with the same intention. Secondly the bpm on the AI track is acurately analysed while the one for my song is not, which could also be an interesting way of analysing the data: can ai analyse ai songs better than human made songs?
| filename | approachability | arousal | danceability | engagingness | instrumentalness | tempo | valence | ai |
|---|---|---|---|---|---|---|---|---|
| ahram-j-1 | 0.2991498 | 3.417260 | 0.2711799 | 0.1026429 | 0.9141049 | 84 | 4.016967 | TRUE |
| ahram-j-2 | 0.1889460 | 4.459196 | 0.4690239 | 0.5624804 | 0.3271964 | 95 | 3.767471 | TRUE |
| aleksandra-b-1 | 0.1644350 | 5.343031 | 0.8357580 | 0.5665221 | 0.3702452 | 68 | 4.738314 | FALSE |
| aleksandra-b-2 | 0.2511401 | 3.680455 | 0.6918470 | 0.1301249 | 0.8842366 | 104 | 4.044941 | TRUE |
| angelo-w-1 | 0.1614367 | 3.621579 | 0.7069914 | 0.3248783 | 0.7907066 | 140 | 3.301473 | FALSE |
| id | approachability | arousal | danceability | engagingness | instrumentalness | tempo | valence | ai |
|---|---|---|---|---|---|---|---|---|
| berend-b-1 | 0.1450785 | 5.021568 | 0.7396224 | 0.5278043 | 0.5858963 | 143 | 4.429538 | TRUE |
| berend-b-2 | 0.2117881 | 5.656832 | 0.6107739 | 0.5786535 | 0.3487158 | 75 | 4.476577 | TRUE |
| desmond-l-1 | 0.2629817 | 4.478108 | 0.2859525 | 0.4156072 | 0.6434987 | 135 | 3.936315 | TRUE |
| desmond-l-2 | 0.2929443 | 5.076702 | 0.3010519 | 0.5524329 | 0.4989389 | 73 | 4.316221 | TRUE |
| evan-l-2 | 0.1081999 | 5.602334 | 0.4800247 | 0.6272448 | 0.5513844 | 135 | 4.445124 | TRUE |
Information on my submitted tracks
Hidde-s-1:
I produced this song myself. I make music with clubs or festivals in mind as I like to DJ. For this track I tried to combine a mainstream house music sound and combine it with some more raw electronic sounds.
Hidde-s-2:
This is a track I generated with Suno. I asked chat gpt what the key characteristics of a dance track in a sweaty club in Amsterdam were:
“Punchy four-on-the-floor kick, deep rolling bass, crisp shuffled hi-hats, sharp claps, detuned wide synth leads, tension-filled breakdown, rising FX, massive sidechained drop, high-energy, club-focused groove.”
Clustering is a technique … Here you can see that there’s is no distinguishable difference between the AI tracks and the human tracks. At least not through clustering based on the dataset. [INSERT REASON:]
Here you’ll find an energy novelty function. This measures blah blah blah as can be seen here.
Spectral novelty text here:
The tempo grams below are based on this novelty function. As you can see
there won’t be a difference since dance music is made with a drum
machine.
Above you can see cepstrograms
##structuringAbove you can see SSMs
# A tibble: 2 × 3
class precision recall
<fct> <dbl> <dbl>
1 AI 0.385 0.357
2 Non-AI 0.437 0.467
above you can see
# A tibble: 2 × 3
class precision recall
<fct> <dbl> <dbl>
1 AI 0.75 0.643
2 Non-AI 0.706 0.8